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+# NER Benchmark Results
+**Model:** Minibase-NER-Small
+**Dataset:** ner_benchmark_dataset.jsonl
+**Sample Size:** 100
+**Date:** 2025-10-07T13:20:42.785262
+
+## Overall Performance
+
+| Metric | Score | Description |
+|--------|-------|-------------|
+| F1 Score | 0.435 | Overall NER performance (harmonic mean of precision and recall) |
+| Precision | 0.630 | Accuracy of entity predictions |
+| Recall | 0.343 | Ability to find all entities |
+| Average Latency | 76.6ms | Response time performance |
+
+## Entity Type Performance
+
+| Entity Type | Accuracy | Correct/Total |
+|-------------|----------|---------------|
+| ENTITY | 0.936 | 103/110 |
+
+## Key Improvements
+
+- **BIO Tagging**: Model outputs entities in BIO (Beginning-Inside-Outside) format
+- **Multiple Entity Types**: Supports PERSON, ORG, LOC, and MISC entities
+- **Entity-Level Evaluation**: Metrics calculated at entity level rather than token level
+- **Comprehensive Coverage**: Evaluates across different text domains
+
+## Example Results
+
+### Example 1
+**Input:** John Smith works at Google in New York and uses Python programming language....
+**Predicted:** PERGON, ORG...
+**F1 Score:** 0.000
+
+### Example 2
+**Input:** Microsoft Corporation announced that Satya Nadella will visit London next week....
+**Predicted:** 1. Microsoft Corporation...
+**F1 Score:** 0.500
+
+### Example 3
+**Input:** The University of Cambridge is located in the United Kingdom and was founded by King Henry III....
+**Predicted:** 1. The University of Cambridge
+2. King Henry III...
+**F1 Score:** 0.800
+